Track fastener defect detection based on improved YOLOv5s algorithm

ZHANG Xingsheng; RUAN Jiuhong; SHEN Benlan; LI Jincheng; HUA Chao

doi:10.3969/j.issn.1672-0032.2025.02.002

Abstract：Aiming at the problems of high complexity of track fastener defects,serious impact on train safety,and low efficiency of manual inspection,a track fastener defect detection algorithm based on computer vision is proposed.Considering the characteristics of track fastener defects and the complex working environment during detection,the ConvNeXt V2 module is used to replace the front-end C3 module of the YOLOv5s algorithm backbone network,the Efficient Rep network is used to improve the back-end of the YOLOv5s algorithm backbone network,and the WIoU loss function with dynamic non-focusing mechanism is introduced to accelerate the convergence speed of the YOLOv5s algorithm model,forming an improved YOLOv5s algorithm(CR-YOLOv5s algorithm)to detect track fastener defect states.Ablation experiments and comparative experiments with faster region-based convolutional neural networks(Faster R-CNN)algorithm,single shot multibox detector(SSD)algorithm,YOLOv3 algorithm,and YOLOv4 algorithm are conducted.The experimental results show that the recall rate of CR-YOLOv5s algorithm is 89.3％,the average detection accuracy is 95.8％,and the average detection time is 10.1 ms,all three indicators are superior to the other four algorithms.Compared with the YOLOv5s algorithm,the CR-YOLOv5s algorithm improves the recall rate by 5.7％,the average detection accuracy by 4.0％,and prolongs the average detection time by 1.0 ms.Considering factors such as track fastener state detection task requirements,recall rate,average detection accuracy,and average detection time,the CR-YOLOv5s algorithm is more advantageous for detecting track fastener defect states.